Derivative imaging for subsurface object detection

ABSTRACT

A series of scans is generated for a subsurface and a derivative image is created using the series of subsurface images. One or more tests are performed on the derivative image, and a subsurface object is detected based on the one or more tests. A sensor is configured to generate a series of scans for a subsurface and a processor is coupled to the sensor. The processor is configured to execute stored program instructions that cause the processor to generate a series of images of the subsurface using the series of scans, create a derivative image using the series of subsurface images, perform one or more tests on the derivative image, and detect a subsurface object based on the one or more tests.

RELATED APPLICATION

This application claims the benefit of Provisional Patent ApplicationSer. No. 61/304,467 filed Feb. 14, 2010, to which priority is claimedpursuant to 35 U.S.C. §119(e) and which is hereby incorporated herein byreference.

SUMMARY

According to various embodiments, methods of the disclosure involvegenerating a series of scans for a subsurface and creating a derivativeimage using the series of subsurface images. Methods further involveperforming one or more tests on the derivative image, and detecting asubsurface object based on the one or more tests.

In accordance with other embodiments, systems of the disclosure includea sensor configured to generate a series of scans for a subsurface and aprocessor coupled to the sensor. The processor is configured to executestored program instructions that cause the processor to generate aseries of images of the subsurface using the series of scans, create aderivative image using the series of subsurface images, perform one ormore tests on the derivative image, and detect a subsurface object basedon the one or more tests.

According to other embodiments, automated subsurface obstacle detectioninvolves scanning a subsurface proximate an underground sensor,generating an image of the subsurface using a first subsurface scan,updating the subsurface image using subsurface scans subsequent to thefirst scan, and calculating a derivative of a parameter of the updatedsubsurface image. The method further involves forming a derivative imageusing derivatives of the parameter, performing one or more tests on thederivative image, and generating an output indicating presence of asubsurface obstacle proximate the underground sensor based on the one ormore tests.

According to some embodiments, automated subsurface obstacle detectioninvolves scanning a subsurface using an above-ground sensor, generatingan image of the subsurface using a first subsurface scan, and updatingthe subsurface image using subsurface scans subsequent to the firstscan. The method further involves calculating a derivative of aparameter of the updated subsurface image, forming a derivative imageusing derivatives of the parameter, performing one or more tests on thederivative image, and generating an output indicating presence of asubsurface obstacle based on the one or more tests.

In further embodiments, automated subsurface obstacle detection involvesmoving a drill head coupled to a drill string along an underground pathusing a drilling machine, and scanning a subsurface proximate the drillhead using a sensor mounted at the drill head. The method also involvesgenerating an image of the subsurface using a first subsurface scan,updating the subsurface image using subsurface scans subsequent to thefirst scan, calculating a derivative of a parameter of the updatedsubsurface image, and forming a derivative image using derivatives ofthe parameter. The method further involves performing one or more testson the derivative image, and generating an output indicating presence ofa subsurface obstacle proximate the drill head based on the one or moretests.

In accordance with various embodiments, automated subsurface obstacledetection for a horizontal directional drilling (HDD) system involvesgenerating a series of scans for a subsurface volume using a groundpenetrating radar (GPR) mounted at a boring tool to create a SAR image.The method also involves creating a derivative image of the SAR imageusing a parameter of the SAR image, performing one or more tests on thederivative image, and detecting presence of an object ahead and/orlateral of the bore tool based on the one or more tests.

In other embodiments, automated obstacle detection for an HDD systeminvolves transmitting GPR probe signals into a subsurface ahead andlateral of the drill head, receiving return signals, and producing a SARimage using the return signals. The method also involves measuring aparameter for each pixel of the SAR image, calculating a derivative ofthe pixel parameter measurement, and forming a derivative SAR imageusing the calculated derivative measurements. The method furtherinvolves testing each pixel of the derivative image for one or morespecified conditions, and generating a detection event signal based onmeeting the one or more specified conditions, the detection event signalindicating presence of an obstacle ahead or lateral of the drill headand falling within a detection zone of the GPR.

According to further embodiments, a system for automated subsurfaceobstacle detection includes a housing configured for above-groundportability, an above-ground sensor coupled to the housing andconfigured for subsurface sensing, and a memory configured to storeprogram instructions for implementing a derivative imaging objectdetection algorithm. A processor is coupled to the memory and thesensor. The processor is configured to execute stored programinstructions for implementing derivative imaging object detectionprocesses comprising: generating an image of the subsurface using afirst subsurface scan; updating the subsurface image using subsurfacescans subsequent to the first scan; calculating a derivative of aparameter of the updated subsurface image; forming a derivative imageusing derivatives of the parameter; performing one or more tests on thederivative image; and generating an output indicating presence of asubsurface obstacle based on the one or more tests.

Other embodiments are directed to a system for automated subsurfaceobstacle detection which includes a housing configured for subsurfacedeployment, a sensor coupled to the housing and configured forsubsurface deployment and subsurface sensing, and a memory configured tostore program instructions for implementing a derivative imaging objectdetection algorithm. A processor is coupled to the memory and thesensor. The processor is configured to execute stored programinstructions for implementing derivative imaging object detectionprocesses comprising generating an image of the subsurface using a firstsubsurface scan; updating the subsurface image using subsurface scanssubsequent to the first scan; calculating a derivative of a parameter ofthe updated subsurface image; forming a derivative image usingderivatives of the parameter; performing one or more tests on thederivative image; and generating an output indicating presence of asubsurface obstacle proximate the housing based on the one or moretests.

According to some embodiments, a system for automated subsurfaceobstacle detection includes an excavation machine comprising a drivingunit coupled to an earth penetrating tool, and a sensor mounted on orproximate the earth penetrating tool. The sensor is configured forsubsurface sensing. A memory is configured to store program instructionsfor implementing a derivative imaging object detection algorithm, and aprocessor is coupled to the memory and the sensor. The processor isconfigured to execute stored program instructions for implementingderivative imaging object detection processes comprising: scanning asubsurface proximate the earth penetrating tool while the earthpenetrating tool advances through a subsurface; generating an image ofthe subsurface using a first subsurface scan; updating the subsurfaceimage using subsurface scans subsequent to the first scan; calculating aderivative of a parameter of the updated subsurface image; forming aderivative image using derivatives of the parameter; performing one ormore tests on the derivative image; and generating an output indicatingpresence of a subsurface obstacle proximate the earth penetrating toolbased on the one or more tests.

In accordance with various embodiments, an HDD system includes a drivingunit, a drill string coupled to the driving unit, a drill head coupledto the drill string, and a cutting tool mounted to the drill head. Aradar sensor is mounted at the drill head proximal of the cutting tool.The radar sensor comprises a transmitter for transmitting radar probesignals into a subsurface and a receiver for receiving return signals. Aprocessor is coupled to memory and the radar sensor. The processor isconfigured to execute program instructions stored in the memory forimplementing derivative imaging object detection processes comprising:generating a SAR image using the received returned signals; generating aderivative image of the SAR image using a parameter of the SAR image;performing one or more tests on the derivative image; and detectingpresence of an object at least ahead of the cutting tool based on theone or more tests.

These and other features can be understood in view of the followingdetailed discussion and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 and 2 show various processes of derivative imaging algorithms inaccordance with various embodiments;

FIG. 3 shows a first frame of a dynamic SAR image reconstructed from rawradar return data resulting from a first scan of a subsurface inaccordance with various embodiments;

FIG. 4 shows the additive effect of updating the SAR image of FIG. 3with SAR image data for each successive scan, resulting in an image withmuch higher resolution in accordance with various embodiments;

FIG. 5 shows the frame just prior to detection of a pre-installedsubsurface object relative to a drill head advancing through thesubsurface in accordance with various embodiments;

FIGS. 6-8 illustrate an example of a mask image applied to a SAR imagein accordance with various embodiments;

FIG. 9 shows a block diagram of a system for implementing a derivativeimaging object detection method in accordance with various embodiments;

FIG. 10 shows a cross-section through a portion of ground where a boringoperation takes place using an HDD machine in accordance with variousembodiments;

FIG. 11 is a block diagram of various HDD system components including adown-hole radar unit proximate to a boring tool in accordance withvarious embodiments;

FIGS. 12-14 illustrate various processes of methods for automatedobstacle detection in accordance with various embodiments;

FIG. 15 is a block diagram showing various components of an HDD systemimplemented for derivative imaging object detection in accordance withvarious embodiments;

FIG. 16 is a block diagram of a Stepped Frequency Continuous Wave radarimplemented as a drill head sensor of an HDD system in accordance withvarious embodiments;

FIG. 17 shows a cutting tool comprising a sensor package which includesa radar sensor, and further shows a radiation pattern of the drill headradar in accordance with various embodiments;

FIG. 18 shows an illustrative cylindrical detection volume definedrelative to the central axis of a bore path, the detection volumerepresenting a subsurface volume that is within the detection zone of adrill head radar in accordance with various embodiments;

FIGS. 19 and 20 show SAR reconstructions as a series of azimuthal slicesin accordance with various embodiments;

FIGS. 21A-21C are displays associated with azimuth slices 1-3 shown inFIG. 20, the displays showing the output, in graphical form, of aderivative imaging object detection algorithm operating on SARreconstructions for each azimuthal slice in accordance with variousembodiments;

FIG. 22 shows a full 3-D SAR implementation, in which a display maypresent a 3-D isovolume having 3-D isovolume contours with derivativeimaging markers in accordance with various embodiments; and

FIG. 23 shows a bombsight view as a bore progresses, the bombsight viewshowing progressive projections from the cylindrical volume shown inFIG. 22 onto a plane cut normal to the bore axis in accordance withvarious embodiments.

DETAILED DESCRIPTION

In the following description of the illustrated embodiments, referencesare made to the accompanying drawings forming a part hereof, and inwhich are shown by way of illustration, various embodiments by which theinvention may be practiced. It is to be understood that otherembodiments may be utilized, and structural and functional changes maybe made without departing from the scope of the present invention.

Systems, devices or methods according to the present invention mayinclude one or more of the features, structures, methods, orcombinations thereof described herein. For example, a device or systemmay be implemented to include one or more of the advantageous featuresand/or processes described below. It is intended that such a device orsystem need not include all of the features described herein, but may beimplemented to include selected features that provide for usefulstructures, systems, and/or functionality.

Embodiments of the invention are directed to systems and methods forautomated underground obstacle detection using derivative imaging.Embodiments of the invention include derivative imaging systems that canbe implemented as stand-alone systems for surveying a subsurface for thepresence of buried objects, such as utilities and manmade or naturalobstacles. In accordance with various stand-alone system embodiments,derivative imaging of a subsurface may be performed in real time,providing immediate underground object detection information whilescanning or surveying the subsurface. In accordance with otherstand-alone system embodiments, derivative imaging of a subsurface maybe performed subsequent to scanning or surveying the subsurface usingdata acquired during subsurface scanning or surveying.

Embodiments of the invention include derivative imaging systems that canbe incorporated into excavation machinery for surveying a subsurface forthe presence of buried objects prior to or during excavation.Embodiments of the invention include underground obstacle detection andpre-collision warning systems, which are preferably implemented for realtime operation and find particular usefulness when incorporated intoexcavation equipment, such as horizontal directional drilling machines.Other system implementations that incorporate derivative imaging inaccordance with the present invention are contemplated. For example,many of the embodiments disclosed herein are directed to sensors thatare adapted for use or deployment underground, such as in a boring toolof an HDD machine system. Other embodiments are directed to sensors thatare adapted for use at or above ground level, such as surface groundpenetrating radar systems.

Underground obstacle detection using derivate imaging in accordance withthe present invention involves acquiring data from a sensor configuredfor subsurface sensing. The data acquired from the sensor, which istypically pre-processed, is used to generate an image of the subsurface.This subsurface image is “built up” by incorporating additional dataacquired from the sensor into the subsurface image, which progressivelyimproves the resolution or information density of the subsurface image.The subsurface image changes and evolves as newly acquired sensor datais added. A derivate imaging algorithm implemented by a processor orother electronic circuitry operates on the subsurface image bycalculating the derivative of a parameter of the subsurface image. Aderivative image is formed using the derivatives of the parameter, andone or more tests are performed on the derivative image. An output isgenerated indicating presence of a subsurface obstacle proximate theunderground sensor based on the one or more tests.

A wide variety of sensors may be employed in systems that implementderivative imaging in accordance with the present invention. In general,suitable sensors include those that can sense for presence of an objectin proximity, but not in contact, with the sensor, which allows forpre-collision detection of the object. Particularly useful sensorsinclude those that generate a probe signal and sense for a reflected orreturn signal. The following is a non-exhaustive, non-limiting list ofrepresentative sensors that may be adapted for underground objectdetection using derivative imaging in accordance with embodiments of theinvention: a radar sensor such as a ground penetrating radar, anacoustic sensor, a seismic sensor, an electromagnetic sensor, a magneticfield sensor, a magnetic resonance (MRI) sensor, a positron emission(PET) sensor, a nuclear magnetic resonance (NMR) sensor, a time-domainelectromagnetic (TDEM) sensor, a resistivity sensor, a permittivitysensor, a conductivity sensor, a thermal sensor, a capacitance sensor, amagnetic field sensor (e.g., magnetometer), and a chemical sensor.

In various embodiments, a single sensor system is employed forunderground object detection using derivative imaging in accordance withthe invention. In other embodiments, two or more disparate sensor systemare employed for underground object detection using derivative imaging.In some embodiments, disparate sensor systems are employed to provideindependent underground object detection information. In otherembodiments, disparate sensor systems are employed to provide compositeunderground object detection information, such as by using one or morefusion algorithms.

Fusion can be implemented at one or several stages during sensor dataprocessing and derivative imaging. In one approach, for example, aderivative image can be formed and a feature derived using data acquiredfrom each of the disparate sensors independently, followed by a featurelevel fusion. In another approach, a subsurface image can be jointlyformed using fused sensor data in a data level fusion, and a derivativeimage can be generated from this subsurface image. Fusion can beimplemented at any step in the formation of the derivative image, fromacquisition of the raw sensor data prior to formation of the derivativeimage, to fusing the derivative images and identifying objects (targets)in the fused image, to forming derivative images of individualsubsurface images and then fusing the targets identified from each ofthose derivative images. Additional details for performing fusion in thecontext of various embodiments of the invention are disclosed in U.S.Pat. Nos. 6,751,553 and 5,321,613, which are incorporated herein byreference.

Embodiments of the invention are directed to methods of automatedsubsurface obstacle detection which involve scanning a subsurfaceproximate an underground sensor and generating an image of thesubsurface using a first subsurface scan. The subsurface image isupdated using subsurface scans subsequent to the first scan. Aderivative of a parameter of the updated subsurface image is calculated,and a derivative image is formed using derivatives of the parameter. Oneor more tests are performed on the derivative image, and an output isgenerated indicating presence of a subsurface obstacle proximate theunderground sensor based on the one or more tests.

The parameter for which derivatives are calculated is preferably areflected or scattered energy parameter associated with an element orregion of the subsurface image. For example, the parameter may bebrightness of a pixel of the subsurface image. By way of furtherexample, the parameter may be brightness of a cluster of pixels of thesubsurface image. Forming the derivative image typically involvescalculating the derivative of the brightness of each pixel or eachcluster of pixels of the subsurface image to form the derivative image.

In some embodiments, forming the derivative image involves forming afirst derivative image using first derivatives of the parameter. Inother embodiments, forming the derivative image involves forming ahigher order derivative image using higher order derivatives of theparameter, such as second derivatives of the parameter. One or moretests are performed on the first or higher order derivative image, andan output is generated indicating presence of a subsurface obstacleproximate the underground sensor based on the one or more tests. Infurther embodiments, first and second derivative images may be formedusing first and second derivatives of the parameter. One or more testsare performed on each of the first and second order derivative images,and an output is generated indicating presence of a subsurface obstacleproximate the underground sensor based on the one or more tests.

It is understood that higher order derivatives, even beyond the secondderivative, can be used for derivative imaging object detection inaccordance with embodiments of the invention. In some embodiments, oneor more tests can involve an algorithm that combines the values ofderivatives of different order in some complex fashion. An example ofthis would be to use the first derivative detection methodologypresented herein, including the tests described, but also include anadditional test that requires the second derivative to have some valueas well in order to accept the first derivative at a particular stage.As another example, a weighted product of the first and secondderivatives may be tested for a certain value to indicate detection. Itdoes happen that the first derivative is positive, while the secondderivative is negative (the second derivative can be thought of as thecurvature of the function in question), indicating that, although thechange in the function is still increasing, the rate of increase isdecreasing. This can be a useful test. For example, tests that use analgorithm that combines the values of derivatives of different order canimprove detection specificity.

In accordance with various embodiments, a derivative imaging algorithmof the present invention includes one or more parameters that can beadjusted to tune algorithm performance. These parameters may be adjustedbased on a number of factors, including geology of the subsurface (e.g.,sand vs. clay), water content, rate of sensor displacement, objectdetection range and depth requirements, object detection sensitivity vs.selectivity (specificity) requirements, tolerance or accuracyrequirements of the detection information, and detection radius or fieldof view (FOV) requirements, among others.

In various embodiments, detection radius represents a parameter of thederivative imaging algorithm that can be adjusted, either automaticallyor with user input. The detection radius parameter establishes thedetection range of the sensor (e.g., field of view). A sensor used forobject detection in accordance with the present invention is preferablyimplemented for sensing ahead and lateral of the sensor. The detectionradius parameter establishes a detection zone extending ahead andlateral of the sensor whose size is dictated by the magnitude of thedetection radius parameter. The shape of the detection zone may also beadjusted using the detection radius parameter and/or other parameter(e.g., a detection zone shaping parameter, such as a parameter thatinfluences the shape of a down-hole radar's radiation pattern). Limitingthe detection radius to a reasonable size in view of particularsurveying conditions is recommended for achieving optimal objectdetection performance.

Selecting a detection radius of 6 feet, for example, causes thederivative imaging algorithm to ignore subsurface features further than6 ft away from the sensor that would otherwise be identified by thealgorithm as likely obstacles. The range of detection radii dependslargely on the type of sensor employed and other factors, such as thoselisted above, with typical detection radii ranging between less than 1ft to at least 20 ft.

Selecting a detection radius that is too small for a particular surveyreduces the size of the sensor's detection zone (i.e., reduces thesensor's range of sensitivity), thus limiting how far beyond the sensorobjects of interest can be detected. This scenario can result in littleor no pre-collision warning (and little or no time to take correctiveaction) if the rate of sensor displacement is high relative to thesensor's range of sensitivity. Reducing the detection radius, however,does increase the selectivity/specificity of object detection, therebyreducing the likelihood of false positives.

Selecting a detection radius that is too large for a particular surveyincreases the size of the sensor's detection zone, which can have thenegative result of detecting objects of little or no concern that poseno threat for collision avoidance purposes. Increasing the sensor'sdetection radius increases the computational burden on the sensorelectronics, signal processing and data communication circuitry, and thedetection algorithm processor, which is wasteful when much of theprocessed information is ignored or discarded for purposes of objectcollision avoidance. Increasing the detection radius, however, doesincrease the sensor's sensitivity to sense objects well ahead or lateralof the sensor, but has the negative effect of reducing theselectivity/specificity of object detection, thereby increasing thelikelihood of false positives.

Another parameter of the derivative imaging algorithm that can beadjusted is a first threshold against which the derivative of eachparameter (e.g. pixel brightness) is tested. For example, the derivativeof the brightness of each pixel or pixel cluster is compared to thefirst threshold. The first threshold is preferably a positive numberequal to or greater than 1, which indicates that pixel brightness isincreasing for successive scans by roughly the same magnitude. The firstthreshold may be set to a positive number that is less than 1, toaccount for presence of noise and clutter, for example.

This first threshold is used to cut off parts of the derivative imagethat are not significant. This is done by applying the first thresholdwithout binarizing the image. In other words, the pixel values below thefirst threshold are set=0, but other values are kept the same as theywere before the first thresholding comparison operation. Thresholdingwith binarization can also be used, but other changes in the settingsand equations in the derivative imaging algorithm would need to beadjusted accordingly, as one skilled in the art would appreciate.

In one representative embodiment, the value of the first threshold isset to 0.7. In general, a pixel brightness derivative value >1 is not tobe expected, as this would indicate that the brightness of the pixel isincreasing the same amount with each added scan. Setting this firstthreshold to 1 or greater would appear to be too stringent (based onexperiments so far), but this is where a second derivative test is ofparticular value. For example, if the rate of change of a pixel value(first derivative) actually increases as scans are added over a numberof scans (meaning the second derivative is >0), then this is a goodindication that something real (e.g., an obstacle) is present in thesensor's field of view.

Another parameter of the derivative imaging algorithm that can beadjusted is a second threshold that impacts the sensitivity (andselectivity or specificity) of the detection algorithm. The secondthreshold is preferably set to allow for detection of changes in thederivative image as it is updated and evolves with the addition of newsensor information with sufficient speed to maintain real timesubsurface object detection. The second threshold may be a value thatrepresents a minimum change (e.g., percent change) in the last nderivatives of the brightness of a particular pixel that is required toconstitute a likely detection event, where n is an integer.

The second threshold determines if a pixel in the derivative image (notthe actual data image of the subsurface) is to be considered a detectionevent, and, as will be described in detail hereinbelow, whether a markeris added to a marker mask (which is also a 2-D array or an “image”) bythe derivative imaging algorithm. The value of the second threshold istypically set and adjusted based on a number of factors, including thevalues of n (i.e., the last “n” derivatives) and the value of the firstthreshold. The value of n can generally vary between 1 and 20, andtypically varies from 5 to 15. In general, smaller values of n providefor an increase in the sensitivity of the detection algorithm, with aconcomitant reduction in selectivity/specificity and increase in thelikelihood of false positives.

In one representative example, the value of n is set to 10, meaning thata series of 10 consecutive derivatives greater than the value of firstthreshold (set at 0.7) are required for a detection. The secondthreshold is set to 10, and is used for thresholding with binarization,which means that all pixels in the derivative image above the secondthreshold are set=1, while all those below the threshold are set=0. Thevalue of this second threshold is tied intimately to the number, n, ofimages used in the detection decision, as well as to the value of thefirst threshold. Recall that the first threshold in this representativeexample is set to 0.7 and applied as thresholding without binarization,so the second threshold determines if the sum of the series of nderivative images, at a particular location, is greater than 10 (orhaving an average value for the 10 images of >1 in this case).

In this representative example, the value of the second threshold=n, butthis does not necessarily have to be the case, and the choices of thefirst threshold, the second threshold, and n work together to determinethe sensitivity and specificity of the derivative imaging algorithm.

In some embodiments, each of the first threshold, the second threshold,and n parameters are selectable. In other embodiments, only a sub-set ofthe first threshold, the second threshold, and n parameters areselectable. In further embodiments, a sub-set of the first threshold,the second threshold, and n parameters are fixed after performingnecessary calibration and unalterable thereafter until a subsequentcalibration is performed. Some or all of the first threshold, the secondthreshold, and n parameters may be selectable by an operator,automatically by a processor implementing the derivative imagingalgorithm based on various inputs, or semi-automatically with input fromthe operator.

According to other embodiments, a single control (e.g., user controlknob) can be implemented that selects each of the first threshold, thesecond threshold, and n parameters for different types of soil orexcavation (e.g., HDD) conditions. For example, moving the control knobor other type of switch by the operator to different discrete positionscan result in setting each of the first threshold, the second threshold,and n parameters to predetermined values associated with the differentdiscrete switch positions (or switch selections).

By way of further example, an analog adjustment approach may be used foradjusting the first threshold, the second threshold, and n parameters.Rather than setting these parameters to predetermined discrete valuesbased on discrete switch positions (or switch selections), movement ofthe control switch or switches can cause these parameters to changeincrementally in a continuous manner. Discrete and analog switchselection approaches can be combined to provide an operator with theability to make course changes (via discrete switch position selection)and fine changes (via incremental continuous switch position adjustment)in these parameters (all or a sub-set of the first threshold, the secondthreshold, and n parameters).

In some embodiments, one or more sensors of the system may be used toevaluate the one or more soil and/or excavation characteristics. Thesensor(s) used for this evaluation may be the same or differentsensor(s) that is/are used for object detection. For example, a GPR canbe used to determine various characteristics of the soil, and aprocessor-implemented evaluation of these soil characteristics canresult in automatic or semi-automatic selection or adjustment (e.g.,dynamically with changing soil/excavation conditions) of all or asub-set of the first threshold, the second threshold, and n parameters.Additional details about characterizing subsurface geology that can beincorporated in a control methodology and system for setting andadjusting one or more parameters of a derivative imaging algorithm ofthe present invention are disclosed in commonly owned U.S. Pat. No.6,701,647, which is incorporated herein by reference.

Each pixel of the derivative image may be tested for specific conditionsrelating to the reasonableness of other aspects of the pixel or pixelcluster. For example, each pixel or pixel cluster can be tested todetermine if the range associated with each pixel or pixel cluster isless than a detection depth of the sensor. If the range is determined tobe beyond the detection depth, this pixel or pixel cluster informationis considered unreliable and is ignored for purposes of determiningwhether or not a detection event has occurred. Each pixel or pixelcluster can be tested to determine if the location associated with eachpixel or pixel cluster is beyond the position of the sensor. If theposition is determined to be behind the sensor position, this pixel orpixel cluster information is considered unreliable and is ignored forpurposes of determining whether or not a detection event has occurred.

In some embodiments, the derivative imaging algorithm is implemented toallow for adjustment of the detection radius and the first and secondthresholds described above. In other embodiments, the derivative imagingalgorithm is implemented to allow for adjustment of the detectionradius, the first and second thresholds, and pixel range testing. Infurther embodiments, the derivative imaging algorithm is implemented toallow for adjustment of the detection radius, the first and secondthresholds, pixel range testing, and pixel location testing.

Turning now to FIG. 1, there is illustrated various processes of aderivative imaging algorithm in accordance with embodiments of theinvention. According to FIG. 1, derivative imaging involves use of anunderground sensor to scan 101 ahead, and preferably lateral, of theunderground sensor. An image of the subsurface is generated and updated103 for each scan. A derivative of a parameter of the image iscalculated 105. A derivative image is formed and updated 107 for eachscan. One or more tests are performed 109 on the derivative image.Presence of an obstacle ahead or lateral of the sensor is detected 111based on the one or more tests.

According to various embodiments, the derivative imaging algorithmgenerates and performs processing on at least two images; a data image(i.e., an image of the subsurface), and a derivative image. One or moretests are performed on the derivative image to detect the presence of asubsurface object. An indicator of the presence of the object (e.g.,marker or tag), as determined from the algorithm operating on thederivative image, is then placed on the processed data image. Thederivative image is generally hidden from the user and is an internalcomponent of the derivative imaging algorithm.

According to embodiments that involve SAR reconstruction, the data imageis the SAR reconstruction, which is an image of what is in the soil.Markers indicative of detection events are placed on this SAR image sothat the operator or a processor can understand where in subsurfacespace the detection has occurred.

FIG. 2 illustrates various processes of a derivative imaging algorithmin accordance with embodiments of the invention. According to FIG. 2,derivative imaging involves use of an underground sensor to perform 120a subsurface scan i, where i is an integer. A check is made at block 122to determine if i>1. If not, then the current scan is the first scan,and an image is generated 124 using the scan i data. The value of i isset to i+1 at block 126. If it is determined at block 122 that i>1, thenthe current image is updated 128 using the scan i data. A check is madeat block 130 to determine if scan i≧3, which is a test for the minimumnumber of scans required to perform a derivative calculation. If not,then the value of i is set to i+1 at block 132 and control returns toblock 120.

If it is determined at block 130 that scan i≧3, then a derivative of animage parameter (e.g., image pixel brightness) is calculated 134. Acheck is made at block 136 to determine if a derivative image exists. Ifnot, then a derivative image is generated 138, i is set to i+1 at block126, and control returns to block 120.

If it is determined at block 136 that a derivative image exists, thenthe current derivative image is updated 140 using the calculatedderivative. One or more tests are performed 142 on the derivative image.If the one or more tests indicate presence of an obstacle ahead orlateral of the sensor, as tested at block 144, then an output isgenerated 146 indicating detection of the obstacle. Otherwise, i is setto i+1 at block 132, and control returns to block 120. The processesshown in FIG. 2 are preferably performed on a pixel-by-pixel basis (orpixel cluster-by-pixel cluster basis) for each scan.

FIGS. 3-8 illustrate synthetic aperture radar images with the results ofthe derivative imaging object detection algorithm superimposed asrectangular markers. In FIGS. 3-8, data was acquired from a radar sensorpackage mounted to a drill head proximal of the drill head spade. Theradar sensor data used to construct the plots shown in FIGS. 3-8 wereacquired with the drill head moving longitudinally without beingrotated.

In FIGS. 3-8, the x-axis represents the distance (in feet) the drillhead has travelled forward relative to the driving source (e.g., HDDmachine). Actual longitudinal displacement of the drill head can bemeasured using an encoder mounted on the HDD machine, by a displacementsensor (e.g., accelerometer or gyro) provided at the drill head, or byuse of an above-ground locator or tracker, for example. According to oneapproach, drill head advance can be measured with a calibrated linearencoder and rotation angle can be measured using a MEMS roll sensorincorporated into the transmitter board of the radar sensor. The y-axisrepresents the distance (in feet) from the drill bore axis. A y-axisvalue of 0 ft represents the drill bore itself. Increasing y valuesindicate increasing distances from the bore.

The images of FIGS. 3-8 show a radial slice extending through a cylindersurrounding the bore. The slice extends from the center of the cylinder(bore axis) radially outward to at least the selected detection radiusthat defines the sensor's field of view. This slice is also oriented ata particular azimuthal angle. For example, a particular slice may rangefrom zero distance from the bore to 6 ft from the bore, in a directionat 180 degrees vertically down (or at an azimuthal angle of 90 degrees,or any other angle).

The SAR images shown in FIGS. 3-8 were computed at a single azimuthalangle. This was done for these data because the data were collected withthe drill head being longitudinally displaced without rotation. Thus,only one azimuthal angle was sampled in these data. The derivative imageis calculated in the background, and when an object is detected by thatalgorithm, a marker is placed on the SAR image itself. As such, theimages of FIGS. 3-8 are SAR images with an object marker inserted wherethe derivative image algorithm has determined that an object exists, orwhere the algorithm has determined that its criteria for the existenceof an object ahead of the drill head have been met.

To look in all directions, a full 3-D SAR image (or image in 3-D of anyof the other sensor data) is computed and the derivative image in 3-Dspace calculated. One approach involves computing a series of radialslices at varying azimuthal angles around the bore (like spokes on awheel), so that the operator or processor can tell or compute not onlythe distance of the object is from the bore, but also the azimuthaldirection to that object. A more detailed discussion of a full 3-D SARderivative imaging approach in accordance with embodiments of theinvention is provided hereinbelow with reference to FIGS. 17-23.

The rectangular markers shown in FIGS. 6-8 represent a detection eventthat occurs prior to the sensor reaching the obstacle. The vertical line150 shown in FIGS. 4-8 is a cursor that indicates the position of thedrill head as it progresses along an underground path that originates ata zero reference of the x-axis and extends along the positive x-axis(i.e., the cursor moves from left to right corresponding to longitudinaldrill head advancement relative to the driving source (e.g., HDDmachine)). A detection zone 151 of the sensor is illustrated as having adetection radius, r_(D), which in this illustrative example is about 4.6ft. It is understood that the shape of the detection zone is forillustrative purposes only, and can be designed to assume a desiredshape about the sensor. It is noted that the detection zone 151 is shownto originate slightly behind the drill head, since the sensor package istypically mounted behind (proximal of) the drill spade.

FIG. 3 is a first frame of a dynamic SAR image reconstructed from rawradar return data resulting from a first scan of the test subsurface. Ascan be seen in FIG. 3, the first frame of the SAR image is of relativelypoor resolution. This first frame, however, represents the initialsubsurface image to which additional SAR image data is added by eachsuccessive scan of the subsurface. FIG. 4, for example, clearlydemonstrates the additive effect of updating the SAR image of FIG. 3with SAR image data for each successive scan, resulting in an image withmuch higher resolution (increased information density).

In FIG. 4, cursor 150 shows the longitudinal progression of the drillhead relative the driving source as the drill head advances toward apre-installed target location about 16 ft ahead of the zero reference ofthe x-axis. In this illustrative scenario, the detection radius is setto about 4.6 ft, meaning that objects that would be identified by thedetection algorithm as likely obstacles further than 4.6 ft from thesensor are ignored. In FIG. 4, the location of the drill head, as shownby cursor 150, is about 2.5 ft from its initial location (i.e., the zeroreference of the x-axis). Because the pre-installed obstacle is wellahead of the drill head sensor and beyond the sensor's detection zone151, presence of the pre-installed target is not detected (i.e., 16ft−2.5 ft=13.5 ft (distance between sensor and target)−4.6 ft (detectionradius)−1 foot (setback from drill spade)=about 7.9 ft, which is about8.1 ft beyond the sensor's detection zone).

In FIG. 5, the drill head has advanced to about 13 ft from the origin,as indicated by the cursor 150. In FIG. 5, the pre-installed target mayeither be close to or slightly within the sensor's detection zone 151.Because either the pre-installed obstacle is still slightly ahead of thedrill head sensor or less than 3 scans of data have been acquired withthe drill head falling within the detection zone 151, presence of thepre-installed target is not detected. In this particular case, the imageshown in FIG. 5 is the frame just prior to detection of thepre-installed obstacle (e.g., scan 2 of a minimum of 3 scans needed tocalculate pixel brightness derivatives and perform pre-collisiondetection of the pre-installed obstacle located ahead of the drillhead).

FIGS. 6-8 illustrate an example of a mask image applied to a SAR image.For each of these figures, a tag or other marker has been added to atarget detection mask after each scan if a positive indication of objectdetection has occurred. In some embodiments, the SAR image may becropped to exclude image data for a region beyond a certain distance infront of or to the sides of the drill head. The target detection mask ispreferably the same size as the cropped SAR image and is applied to thecropped SAR image. The processor implementing the derivative imagingobject detection algorithm may perform an action based on the generationor application of a marker. If only one marker is applied, for example,then the processor may issue a warning to the operator of the imagingsystem (e.g., an HDD machine operator via a human-machine interface).The processor of an HDD machine, by way of further example, could stopthe drill if more than one marker was applied and/or at least one markerwas close in proximity to at least one other marker, for example. Themore markers that are present and the denser the markers are, the moreconfidence the processor has in making a correct object detection.

FIG. 6 shows a first detection event that occurs at about 13.5 ft fromthe origin, which is about 2.5 ft ahead of the pre-installed obstaclelocated at 16 ft from the origin. This first detection event isindicated by the rectangular marker 152. FIG. 7 shows a second detectionevent that occurs at about 14 ft from the origin, which is about 2 ftahead of the pre-installed obstacle. This second detection event isindicated by the rectangular marker 154. As is discussed above,reconstructed SAR image data is continuously added to the composite SARimage on a scan-by-scan basis, which continuously improves theresolution of the composite SAR image, as can clearly be seen in FIGS.3-8. The second and any subsequent detection events are indicated byadditional markers, which serve to reinforce or confirm the reliabilityof the first detection event. FIG. 8 shows a marked SAR image showingall detection events for possible obstacles that were identified by thederivative imaging object detection algorithm. In particular, FIG. 8shows the last frame of the updated SAR image with markers from thederivative image object detection algorithm superimposed thereon.

In response to a detection event or n events, various actions may occur.For example, a human perceivable warning may be generated in response toa first detection event, and different warnings of varying intensity orcharacter may be generated in response to each subsequent detectionevent. An early warning algorithm executed by a processor of the HDDmachine, for example, may generate a warning light and/or audible alarmin response to a detection event. The warning may prompt the operator totake corrective action to avoid a collision between the drill head andthe detected obstacle located ahead of the drill head. If no orinsufficient corrective action is taken, such as halting or changingdirection or displacement rate of the drill head, the early warningalgorithm may automatically slow the drill displacement rate or haltadvancement of the drill head. In a conservative implementation, thecontrol protocol may simply halt movement of the drill head in responseto the first detection event.

A tiered set of HDD machine control protocols can be programmed andexecuted by the processor for automatically intervening in HDDoperations in response to successive detection events. The level ofautomatic corrective action intervention made by the HDD machinecontroller may increase in response to each successive detection event.For example, in response to a first detection event, a first controlprotocol can be implemented by the HDD machine controller to slow downthe drilling rate, such as by slowing longitudinal displacement of thedrill head and, if desired, reduce the rate of drill head rotation. Anoperator alarm is preferably generated in response to the firstdetection event. In response to a second detection event, moreaggressive intervention is taken by the HDD machine controller inaccordance with a second control protocol, such as by disabling drillhead movement and generating an alarm of increased intensity. Othercontrol scenarios are contemplated.

Actual longitudinal displacement of the drill head can be measured usinga linear encoder mounted on the HDD machine, by a displacement sensor(e.g., accelerometer or gyro) provided at the drill head, or by use ofan above-ground locator or tracker. Drill head rotation can be measuredby these and other sensors, such as a MEMS roll sensor incorporated intothe radar sensor electronics. Additional details of drill headdisplacement and rotation measuring methodologies, systems, and devicesthat can provide drill head positioning and rotation data for use by aderivative imaging algorithm of the present invention are disclosed incommonly owned U.S. Pat. Nos. 7,607,494, 6,755,263, and 7,182,151, allof which are incorporated herein by reference.

Reference will now be made to FIGS. 17-23, which are provided to enhancean appreciation of various physical, geometric, and other factors thatare dealt with when performing 3-D subsurface derivative imaging inaccordance with embodiments of the invention. These factors are ofparticular interest when performing 3-D subsurface object detectionusing a sensor that is both displaced longitudinally and rotated as itis moved through the ground.

FIG. 17 shows an embodiments of a cutting tool configured to displaceearth as it is forcibly moved through a subsurface. For purposes ofillustration, one particular cutting tool contemplated in FIG. 17 is aboring tool 712, it being understood that other types of earthpenetrating implements are within the scope of the present invention(e.g., pneumatic piercing tools, navigable moles, reamers, etc.).

The boring tool 712 shown in FIG. 17 includes a drill head 715 and adrill spade 707 positioned at a distal end of the drill head 715. Asensor package 718 is mounted in or on the drill head 715 at a locationproximal of the drill spade 707. The sensor package 718 may house one ormore sensors of similar or disparate type. The sensor package 718 alsoincludes communication electronics and a processing capacity, which canvary in terms of sophistication based on design particulars of the drillhead 715. The communication electronics may be configured for some formof wireless communication with an above-ground received (e.g.,transceiver), but is preferably configured to communicate with anabove-ground system via a wireline link established along a drill stringwhich couples the drill head 715 with a driving source (e.g., an HDDmachine).

For example, the wireline link can be realized using a communicationprotocol that operates over the HomePlug™ wireline interface, whichallows for control of the drill head sensor(s) via a human-machineinterface. In various embodiments, the electronic components of theboring tool 712 are coupled to a communication medium capable oftransmitting power and data between the down-hole sensor hardware and anabove-ground source. A suitable communication medium is DCI CableLink®,which is available from Digital Control Incorporated. The CableLink®system is permanently installed into the drill rods of the drill stem sothat the mechanical and electrical connections occur automatically whenthe rods are threaded together.

As previously stated, various types of sensors may be housed in a sensorcompartment of the drill head within which the sensor package 718 issituated. Some sensors may include a sensor element that is exposed toearth adjacent the drill head 712, while others are enclosed within thesensor compartment. Useful sensors include any of those listed above,for example, and those disclosed in the U.S. patents that areincorporated herein by reference.

In various embodiments, the sensor package 718 comprises a radar sensor.FIG. 17 shows a radiation pattern 710 of the drill head radar 718 thatis dependent on angle and, as such, preferentially detects returns froman angle or range of angles. The size and shape of the radiation pattern710, which defines the radar's detection zone, is largely defined by theantennae design, and may be adjusted using the detection radiusparameter and/or other parameter.

FIG. 18 shows an illustrative cylindrical detection volume 700 definedrelative to the central axis of a bore path 705. This detection volume700 represents a subsurface volume that is within the detection zone ofthe drill head radar, noting that the drill head, and therefore thedrill head radar, is subject to both longitudinal advancement androtation when creating a bore.

FIG. 19 shows initial SAR reconstructions as a series of azimuthalslices 725. These slices are best shown in FIG. 20. In this illustrativeexample, FIGS. 19 and 20 show five azimuthal slices (numbered slices 1through 5) each associated with a different azimuth. It is noted thatthe periphery of the cylinder 720 represents the limit of the radarsensor's detection zone, and that only fives azimuthal slices are shownfor simplicity of explanation.

FIGS. 21A-21C are displays associated with azimuth slices 1-3, it beingunderstood that similar displays are generated for azimuth slices 4 and5. The displays of FIGS. 21A-21C show the output (in graphical form) ofthe derivative imaging object detection algorithm operating on the SARreconstructions for each of the azimuthal slices 1-5. In the displayshown in FIG. 21A, only noise or clutter data are shown (which can besuppressed to simplify the display). No object ahead of the drill headwas detected by the derivative imaging algorithm for azimuthal slice 1.

For the display shown in FIG. 21B, an object ahead of the drill head(Object A) was detected by the derivative imaging algorithm forazimuthal slice 2. In response to each detection event for azimuthalslice 2, the derivative imaging algorithm placed a marker at theappropriate location of the display. For the display shown in FIG. 21C,an object ahead of the drill head (Object B) was detected by thederivative imaging algorithm for azimuthal slice 3. In response to eachdetection event for azimuthal slice 3, the derivative imaging algorithmplaced a marker at the appropriate location of the display.

Although not shown in FIGS. 21A-21C, the derivative imaging algorithmcontinues to perform object detection for the remaining azimuthal slices4 and 5, and continues processing of azimuthal slices as the drill headadvances longitudinally along the bore path. It is noted that Objects Aand B shown in FIGS. 21B and 21C are different because they lie atdifferent azimuths or they are the projections of the same extendedlinear object projected onto two planes.

In a full 3-D SAR implementation, an example of which is shown in FIG.22, the display may present a 3-D isovolume 750 having 3-D isovolumecontours with derivative imaging markers. In addition, as the boreprogresses, a bombsight view (similar to those used for weather radars)can show progressive projections from the cylindrical volume 750, onto aplane cut normal to the bore axis. An example of such a bombsight viewis shown in FIG. 23, where azimuth is given by angle around the centerand the radial dimension is distance form the bore.

Referring back to FIG. 9, this figures shows a block diagram of a system200 for implementing a derivative imaging object detection method inaccordance with embodiments of the present invention. The embodimentshown in FIG. 9 represents a stand-alone system for surveying asubsurface for the presence of buried objects, such as utilities andmanmade or natural obstacles. Although shown as a stand-alone system forsurveying a subsurface, the system embodiment of FIG. 9 may be used withvarious excavation equipment, including earth penetrating machinery suchas HDD machines and trenchers. For example, the stand-alone system 200may be used by an operator in advance of the excavation operation todetect presence of buried obstacles ahead of an advancing excavationmachine. The stand-alone system 200 may be pulled or pushed by aseparate vehicle or mechanism ahead of the excavation machine or by theexcavation machine itself, providing real time imaging and detection ofburied obstacles ahead of the excavation machine.

The system 200 includes a housing 204 configured for above-groundportability. The system includes a processor 207 coupled to a memory 208and a display 210. The processor 207 is coupled to a derivative imagingobstacle detector 203, which implements derivative imaging algorithms inaccordance with those disclosed herein.

In one configuration, the sensor package 206 is physically andcommunicatively coupled to the housing 204. In another configuration,the sensor package 206 may be physically separate from the housing 204but communicatively coupled therewith. A communication link between thehousing 204 and the sensor package 206 may be a hardwired or a wirelesslink.

The system 200 is configured for portability, and may include atransport arrangement (not shown) such as a wheel arrangement or cart.The transport arrangement may be a harness arrangement that allows theoperator to carry the system 200 while walking over an area to bescanned. A stabilization arrangement may be provided to attenuate orlimit system movement resulting from operator jostling or instability.

The system 200 includes one or more above-ground or surface sensors 206.As was discussed previously, representative above-ground or surfacesensors 206 that may be adapted for subsurface object detection usingderivative imaging in accordance with embodiments of the inventioninclude a GPR sensor, an acoustic sensor, a seismic sensor, anelectromagnetic sensor, a magnetic field sensor, an MRI sensor, a PETsensor, an NMR sensor, a TDEM sensor, a resistivity sensor, apermittivity sensor, a conductivity sensor, a thermal sensor, acapacitance sensor, a magnetic field sensor (e.g., magnetometer), and achemical sensor.

In the embodiment shown in FIG. 9, the sensor 206 transmits a probesignal 209 that propagates through the subsurface and impinges on orilluminates an underground object, in this case a utility 223.Interaction between the utility 223 and the probe signal 209 results ina return signal 211 that is detected by the sensor 206.

Derivative imaging software is preferably stored in the memory 208 andcomprises program instructions executable by the derivative imagingobstacle detector 203 in accordance with derivative imaging algorithmsdescribed herein. The derivative imaging obstacle detector 203 operateson the return signals 211 received by the sensor 206. The derivativeimaging obstacle detector 203 may be implemented in software, hardware,or a combination of software and hardware. The derivative imagingobstacle detector 203 may be integral to the processor 207 or may beimplemented as a component separate from, but communicatively coupledwith, the processor 207. Output from the derivative imaging obstacledetector 203 can be presented on the display 210 (see, e.g., imagesshown in FIGS. 3-8). Output from the derivative imaging obstacledetector 203 can also be communicated (via hardwire or wirelessconnection) to an external system, such as a PC, PDA, smartphone,network, geographic information system (GIS), or utility mapping system.

In various stand-alone system embodiments, derivative imaging of asubsurface may be performed in real time, providing immediateunderground object detection information while scanning or surveying thesubsurface. In other stand-alone system embodiments, derivative imagingof a subsurface may be performed subsequent to scanning or surveying thesubsurface using data acquired during subsurface scanning or surveying.It is noted that in these and other embodiments, derivate imagingalgorithm execution and processing may be performed by an on-board orlocal processor, or by a remote processor, such as a laptop or networkprocessor.

In accordance with various embodiments of the present invention, aderivative imaging system is incorporated as a component of a horizontaldirectional drilling machine. HDD machines are used to install utilitiesunderground. Unfortunately, the use of drills in urban environments hasthe risk of striking and damaging pre-existing utilities. HDD machineembodiments of the present invention employ a radar unit designed to beinstalled on the HDD drill head and used to determine the presence ofobstacles in or nearby the boring path. Transmit and receive antennasare mounted on the drill shaft, behind the drill head spade, andtransmit both ahead and to the side of the drill head. Data can becollected at up to 50 traces per second, and all processing and displayis preferably done in real time.

Horizontal directional drilling provides numerous advantages over thehistorical trench based techniques, for subsurface utility installation.However, HDD does suffer from the constant threat of striking unknown,unmapped, or mis-located utilities and other obstacles. Striking theseobstacles can cost the operator revenues, for repairs, or in moreserious cases result in loss of equipment, injury, or death. Thus, thereis a need for sensors that can be mounted on the drill head that detectobstacles far enough in advance to allow the drill operator to detectand/or map them. A derivative imaging object detection systemincorporated into an HDD machine provides for detecting and/or mappingfeatures or obstacles to allow their avoidance, which is of greatimportance, especially when damaging one of these features could resultin disruption of utility service or possible contaminant release.

In general, underground urban utility corridors are becoming more andmore congested. In these environments, smaller HDD rigs and shorter borelengths are used to install new utility lines. Sensors to detectpossible obstructions must therefore solve the partially contradictoryrequirements of being mounted on small drills, be used in congestedareas, detect obstacles of varying materials and sizes, and yet notnegatively impact production rates or be prohibitively expensive. Wheredrills are used to penetrate and assess the condition of waste sites orareas of potentially contaminated soils, penetration of undergroundstorage tanks or drums could potentially result in release of toxicmaterial and substantial environmental harm. Thus, any sensor must becapable of detecting not only linear, pipe-like targets, but alsoequi-dimensional targets like storage drums and tanks.

New and existing HDD machines can be equipped with an obstacle avoidancesystem of the present invention that includes a modified drill headcontaining a radar sensor package that can replace standard drill headswith a minimal amount of special fittings, tooling, and conversionpackages. By way of example, relatively compact radar sensor packagesare required to fit smaller class HDD machines, such as the VermeerD7x11A and D₂₀x22 Series II, which are typically used for shorter boresin more congested ‘last-mile’ installations. Important considerations tothe functionality of such a radar sensor system are the power andcommunication transmissions that interface power generation, control,processing and operator interface hardware through the drill string tothe drill head sensor.

Among the more important features of an obstacle avoidance system forHDD is the ability to acquire, process, and display information in realtime, as the system is preferably implemented as an early warning deviceto operate during the drilling process. The system is preferablyimplemented to detect obstacles within its sensor field of view wellenough in advance of the drill head so that the operator can stop theadvance of the drill, and assess the situation. To achieve thisrequirement, the received sensor data must be acquired continuously, andconverted into some form of an “image” for easy interpretation directlyby the operator, and/or by the obstacle avoidance computer system. Thisis an important constraint on the design for the sensor hardware,software, antennas, and detection algorithm.

According to various embodiments of the present invention, a SteppedFrequency Continuous Wave (SFCW) radar is used to determine the presenceof obstacles in or nearby a boring path of a drill of an HDD machine.SFCW radar data is used by a processor implementing derivative imagingsoftware to form a subsurface image from a series of scans containingraw data that together form a synthetic aperture radar image. The SARimage may then be analyzed to determine time-dependent changes to theimage as it is formed scan by scan. These changes in the evolving imageassociated with real targets can be identified well before the drillhead position reaches the actual target location. These and otheradvantageous features of the present invention will now be described ingreater detail and in reference to the accompanying drawings.

FIG. 10 shows a cross-section through a portion of ground where a boringoperation takes place. The underground boring system, generally shown asthe boring machine 12, is situated above ground 11 and includes aplatform 14 on which is situated a tilted longitudinal member 16. Theplatform 14 is secured to the ground by pins 18 or other restrainingmembers in order to resist platform 14 movement during the boringoperation. Located on the longitudinal member 16 is a thrust/pullbackpump 17 for driving a drill string 22 in a forward, longitudinaldirection as generally shown by the arrow. The drill string 22 is madeup of a number of drill string members 23 attached end-to-end. Alsolocated on the tilted longitudinal member 16, and mounted to permitmovement along the longitudinal member 16, is a rotation motor or pump19 for rotating the drill string 22 (illustrated in an intermediateposition between an upper position 19 a and a lower position 19 b). Inoperation, the rotation motor 19 rotates the drill string 22 which has aboring tool 24 attached at the distal end of the drill string 22.

A typical boring operation can take place as follows. The rotation motor19 is initially positioned in an upper location 19 a and rotates thedrill string 22. While the boring tool 24 is rotated through rotation ofthe drill string 22, the rotation motor 19 and drill string 22 arepushed in a forward direction by the thrust/pullback pump 17 toward alower position into the ground, thus creating a borehole 26. Therotation motor 19 reaches a lower position 19 b when the drill string 22has been pushed into the borehole 26 by the length of one drill stringmember 23. A new drill string member 23 is then added to the drillstring 22 either manually or automatically, and the rotation motor 19 isreleased and pulled back to the upper location 19 a. The rotation motor19 is used to thread the new drill string member 23 to the drill string22, and the rotation/push process is repeated so as to force the newlylengthened drill string 22 further into the ground, thereby extendingthe borehole 26.

Commonly, water or other fluid is pumped through the drill string 22(refereed to herein as mud) by use of a mud pump. If an air hammer isused, an air compressor is used to force air/foam through the drillstring 22. The mud or air/foam flows back up through the borehole 26 toremove cuttings, dirt, and other debris and improve boring effectivenessand/or efficiency.

A directional steering capability is typically provided for controllingthe direction of the boring tool 24, such that a desired direction canbe imparted to the resulting borehole 26. By these actions, and variouscombinations of these basic actions, a boring procedure can advance aboring tool 24 through soil, including advancing the boring tool 24through a turn.

Because HDD typically does not bore a hole very far from the surface ofthe ground, many belowground obstacles (e.g., sewers, electrical lines,building foundations, etc.) must be maneuvered around. As such, manyboring tools are configured to allow the bore path to turn (e.g., left,right, higher, lower) to curve the bore path around undergroundobstacles.

In accordance with embodiments of the invention, the system alsoincludes an encoder 19 c to monitor of the position of the boring tool24. As the drill head 24 is pushed into the ground, a cable plays outand advances the encoder 19 c, providing the system software with ameasure of the drill head location and triggering radar electronics atdiscrete distance intervals.

FIG. 11 is a block diagram of various HDD system components 200 inaccordance with embodiments of the invention. The HDD system of FIG. 11includes down-hole radar unit 202, such as a GPR unit, proximate aboring tool 201. An exemplary GPR unit well suited for incorporation ina boring tool 210 in the context of various embodiments of the inventionis disclosed in U.S. Pat. No. 7,013,991, previously incorporated hereinby reference. The boring tool 201 may house an orientation sensor and/ora displacement rate sensor, such as a single- or multi-axisaccelerometer, gyroscope, or magnetometer, for example. The boring tool201 may also include one or more geophysical sensors, including acapacitive sensor, acoustic sensor, ultrasonic sensor, seismic sensor,resistivity sensor, and electromagnetic sensor, for example. Use of adown-hole GPR system provides for the detection of nearby buriedobstacles and utilities, and characterization of the local geology.

According to an embodiment of the invention, a controller 213 is coupledto the HDD machine 205 which can be responsible for controllingunderground object detection as described herein. The controller 213 caninclude a processor 207 and memory 208. The memory 208 can be a computerreadable medium encoded with a computer program, software, computerexecutable instructions, instructions capable of being executed by acomputer, etc., to be executed by circuitry, such as processor 207.

Execution of the computer program by the processor 207 causes theprocessor 207 to convert raw captured data into early warnings forobstacle avoidance. The raw data output from the processor 207 may be inthe form of a series of complex 1-D scans with an amplitude value and aphase value, with reflection measurements at each frequency step withinthe measurement bandwidth. Typically, the complex scan vectors for aparticular azimuthal angle of the drill head may be treated as columnvectors, and may be stacked horizontally to form a pair of 2-D maps ofthe data, one for the real, and one for the imaginary part. Full 3-Dcomplex data maps can be created by collecting a series of these 2-Dmaps, with one for each azimuthal angle at which data is collected asthe drill head rotates. In these raw data maps, the horizontal dimension(the scan dimension) is in units of linear position of the drill head,and the vertical dimension is the transmitted frequency.

The computer executable instructions may also cause pre-processing andpreparation of the raw sensor data. This pre-processing may includepre-filtering of the column vectors using a Hamming or other optimalsmoothing window, plus a specially tailored filter that improves thecontrast for certain features. This may be adjusted to fit specific soilconditions or target types. The computer executable instructions mayalso cause the reconstruction of the return signals. This may be done bycalculation of the inverse Fast Fourier Transform (FFT) of the complexcolumn vectors.

The computer executable instructions may additionally causepost-processing of the reconstructed signals. Several steps may beperformed during post-processing. Phase I background subtraction canremove low-spatial-frequency background signals and noise because ituses a very large moving average window, but includes only the currentscan, and scans that were taken prior to it. Phase II backgroundsubtraction may also be used. This is a small moving average windowcentered around the current scan and can remove high-spatial-frequencybackground noise. Time offset correction of the reconstructed returnsignal may additionally be performed, if necessary. Scaling of thereconstructed column vectors may optionally be performed. This is anattempt to normalize the energy of the return signal for each scan. Itintroduces an artifact because, in reality, certain scans will naturallyreturn less energy if the medium is non-reflective or absorbing, forexample, however the artifact can be desirable for visualizing certainobjects in certain conditions. This processing step may have the abilityto be turned on or off as needed. Additional smoothing and medianfiltering may then be done to the resulting signal.

The computer executable instructions may additionally run a SAR imagereconstruction algorithm to build up an image for each 2-D azimuthalslice. The image is compiled scan-by-scan as the drill advances and datacollected. The image is updated in real time. Some SAR pre-processingmay be applied to each scan prior to adding the contribution of the scanto the reconstruction. This pre-processing could be a series of filters,including spatial filters that cut off signals that are at very longrange, and suppressing the amplitude of the signals that are nearrange=0 (nearest the drill head).

The computer executable instructions may then run a target recognitionalgorithm. This algorithm can use the properties of the evolving SARimage as each new scan is processed and added to the reconstruction. Theprocessor looks at the time-dependent (which is also scan-dependent)change in the SAR image as it evolves. The magnitude of the change maycause a positive detection of an object.

The computer executable instructions may additionally create a maskimage and apply markers whenever object detection occurs. This maskimage can be updated after each scan retaining the markers present fromprevious scans. This mask image can also be applied to the SAR image forfurther evaluation.

FIG. 12 illustrates a flow chart in accordance with embodiments of thepresent invention. The flow chart describes a method 300 that includesgenerating 301 a series of radar scans using a GPR mounted bore tool fora subsurface volume. These scans may be used to build up a SAR imagescan by scan. A derivative image of the SAR image may be created 302using a parameter of the SAR image such as the change in time of a SARimage. The derivative can be calculated by using the mean value theorem,taylor expansion, cubic spline, curve fitting, among others. One or moretests are then performed 303 on the derivative image. The presence of anobject at least ahead of the boring tool may then be detected 304 basedon the one or more tests. These will be described in more detail below.

FIG. 13 illustrates another flowchart in accordance with variousembodiments of the present disclosure. The flowchart of FIG. 13illustrates a method 400 for automated obstacle detection. In block 401a series of radar probe signals are transmitted using SFCW GPR. Returnsignals are then received 402. Pre-processing of the return signals mayadditionally be performed. Return signal reconstruction andpost-processing of the reconstructed signals may also be performed. Thereturned signal scans are used to produce 403 a SAR image. The SAR imagemay additionally be cropped to illustrate a region within a certaindistance in front of or to the sides of the drill head. The amount ofreturned signal energy for each pixel in the composite SAR image is thenmeasured 404. A rate of change of the amount of returned signal energyis calculated 405 for each pixel of the SAR image. These derivativecalculations are then used to form 406 a derivative image.

The derivative image may contain the time-dependent (or scan-dependent)change in the SAR image as it evolves. In addition, a second or higherderivative image may also be created. Each pixel of the derivative imageor higher order derivative image is then tested 407 for one or morespecified conditions with regard to the composite SAR image. The systemmay then generate 408 a detection event signal, which indicates thepresence of an obstacle in proximity with the drill head, in response toat least one pixel in the derivative image or higher order derivativeimage meeting one or more specified conditions.

In additional embodiments of method 400, one or more specifiedconditions may be adjusted. Adjustments to the specified conditions cantake place during initial system setup/calibration and/or “on the fly”during field operation. One or more thresholds may be set and/oradjusted automatically based on various properties of the soil, such asthe dielectric constant. This allows for selection of optimum parametersfor creating an image in a particular type of medium.

FIG. 14 illustrates various processes of a method for automated obstacledetection in accordance with embodiments of the present invention. Theprocesses shown in FIG. 14 are preferably implemented in a systememploying an SFCW radar. The method illustrated in FIG. 14 involvesreconstructing 450 a radar return signal for scan i, where i is aninteger, and pre-processing 452 the scan i data for SAR imaging. Thecontribution of the scan i data is added 454 to the SAR image. Anunmarked full SAR image is preferably retained 456 in memory.

The SAR image is preferably cropped 458 to excise the portion of the SARimage that is beyond desired detection range. The derivative of thebrightness of each pixel in the cropped SAR image is calculated 460 toform a derivative image. Each pixel in the derivative image is tested462 for following specific conditions. In some embodiments, all of thefollowing specific conditions must be met. In other embodiments, somebut not all of the following specific conditions must be met:

-   -   is the derivative>first threshold?    -   were the last n derivative of this pixel also>second threshold?    -   is range of pixel<detection depth?    -   is location of pixel>drill head position ?

If all or a sub-set of these specified conditions are met, a marker isadded 464 to the target detection mask to highlight locations ofpotential targets/obstacles. The target detection mask is applied 466 tothe cropped SAR image, and the marked SAR image is displayed 468 to showthe possible target/obstacle locations. If all or the sub-set of thesespecified conditions are not met, control returns to block 450.

FIG. 15 is a block diagram showing various components of an HDD systemimplemented for derivative imaging object detection in accordance withembodiments of the invention. The HDD system component shown in FIG. 15include a boring tool 501, a GPR unit 502, a HDD machine 505, and acontroller 506, which are described hereinabove with reference to otherfigures. FIG. 15 also shows a human-machine interface (HMI) 507, adisplay 508, and an object warning interface 509 coupled to the display508.

According to various embodiments, the HMI 507 provides the interfacebetween the drill operator and radar sensor. The HMI 507 commands maycontrol the radar via a simple text based protocol that operates overthe drill string. The HMI 507 may provide functionality to configure thevarious modes of the system, the reconfigurable run time processingengine, and the display and storage of the data, for example. The HMI507 software may also be provided with a measure of the drill advance,via an encoder for example. The acquired radar images may be displayedin real time on a display 508 coupled to the HMI 507 as each new scan isacquired and processed.

Some embodiments my further include an object warning interface ordevice 509 coupled to the display 508. The warning interface/device 509can provide a warning when an object is detected. The warning could bepresented in a visual way such as a light, for example, or there couldbe an auditory or tactile warning. The object warning interface/devicemay also use marker generation as a trigger. This could include thenumber or density of markers generated on the mask image.

Referring now to FIG. 16, a block diagram of the SFCW HDD sensor 600 inaccordance with embodiments of the invention is shown. The componentsshown in FIG. 16 include an HMI 602, a wireline 603, and a drill headsensor 601. The drill head sensor 601 is shown to include acommunication and radar controller (CTL) 604, a sampler and controller(SMC) 605, a radar transceiver (TRX) 606, a calibration module (RFE)607, and an antenna interface 608.

In accordance with various, the HMI 602 may be used for the control,display and processing of the received radar data. The HMI 602 isconnected via wireline 603 to the drill head sensor 601. The drill headsensor 601 measures the distance to targets using SFCW modulation. TheSFCW sensor is realized using a dual synthesizer heterodyne architecturewith digital IQ detection; the functionality of which is split betweenthe SMC 605 and the radar transceiver 606. The radar transceiver 606includes transmit and receive synthesizers offset in frequency by theradar intermediate frequency (IF), along with required amplifiers andmixers to beat the frequency down to the IF, which is then sampled bythe SMC 605 and converted to an analytic signal. The radar transceiver606 can be designed to operate from 700 to 1700 MHz, with a receiverdynamic range in excess of 100 dB, for example.

In various embodiments of the invention, the transceivers are connectedvia a calibration module 607 to TX/RX bistatic radar antennas 608. Theantennas are preferably designed to match soils with a wide range ofpermittivities. The RFE 607 both matches the radar impedance to that ofthe antenna 608 and provides a cable loop and terminations that allowfor simple calibration of the radar sensor, including a reference fortime zero at the inputs to the antennas 608. The CTL 604 is the mainradar controller in the embodiment illustrated in FIG. 16. The CTL 604configures and controls all the radar modules and implements thecommunication protocol that operates over a HomePlug™ wireline interfaceand allows for control of the radar drill head sensor 601 via the HMI602.

An exemplary implementation will now be described for purposes ofillustrations and not of limitation. With general reference to FIGS. 10,11, and 15-17, the following exemplary implementation will be describedin the context of HDD machine installation and operation. In thisillustrative example, the drill head is designed in two segments. Thesegment closest to the nose of the drill head is the antenna module. Theantenna module requires a space approximately 14 inches long, 1 inchwide, and 1.3 inches deep. Directly behind the antenna module is thesecond segment for the electronics module. The radar electronics module,including rotation sensor, power supply, and communication hardware isabout 26 inches long, 1 inch wide, and 1.25 inches high. Both modulesare designed to be “drop-in” packages that can be quickly and easilyinstalled in the drill head.

A drill head that is 3 inches in diameter and 59 inches in length ismore than sufficient to accommodate these components. Smaller drillheads may be desired when using smaller HDD machines. It is noted that adrill head with a larger diameter provides additional strength whiledrilling in view of the relatively large cavities required for the radarhardware. A traditional HDD operation requires a locating sonde placedinside the drill head to provide information to the drill operatorregarding location, depth, and orientation of the drill head. A locatingsonde may be included in some embodiments, in which case the sonde wouldbe attached in a separate housing directly behind the drill head. As wasdiscussed previously, an adaptation of the DCI CableLink® is permanentlyinstalled into the drill rods so that the mechanical and electricalconnections occur automatically when the drill rods are threadedtogether.

During operation of the HDD machine, the drill head, with the radarelectronics and antenna module enclosed, is attached to the end of thefirst drill rod by means of an adapter rod. All drill rod is equippedwith the CableLink® system so the mechanical and electrical connectionsoccur automatically when additional rods are threaded together duringthe drilling process. On the drill, the drill rod is connected to thedrive chuck and commutator ring assembly. Radar signals and power aretransmitted along the drill string through the commutator ring andtwo-conductor cable to the field instrumentation case. The fieldinstrumentation case is interfaced with the data acquisition computer,distance encoder, and a 48 volt power supply. This equipment may bepositioned on or next to the drilling machine during data collection.

The linear encoder is mounted to the HDD machine and connected to thedrill chuck in order to log the advance of the drill rod and trigger theradar at controlled distance intervals. As the drill rod is pushed intothe ground, a cable plays out and advances the encoder, providing theHMI software with a measure of the rod advance and triggering the radarelectronics at discrete distance intervals. As the drill head is pushedinto the ground, the radar images are acquired and displayed in realtime on the computer display.

After a 10 ft section of drill rod is inserted into the ground, theoperator typically pauses the radar data acquisition and disconnects thedrill chuck from the drill string. This action temporarily disconnectsthe power supply to the radar electronics. Prior to the addition ofanother drill rod, the encoder cable retracts as the drill chuckretreats towards the back of the drill. As another rod is inserted andconnected to the drill string, power is restored to the radarelectronics and the operator resumes data collection. The drill stringis pushed and rotated as necessary along the intended bore path. Thisprocess is repeated until the entire bore is completed.

At the completion of the bore, the drill head is removed and abackreamer, sized slightly larger than the product being installed, isattached. A swivel attaches multiple sizes and styles of pipe or cableto the backreamer. At the directional drill, each rod is subsequentlypulled back and removed until the backreamer and product are pulledthrough the entire bore path.

Embodiments of the invention are directed to systems and methods ofmapping underground utilities that have been detected using derivativeimaging methodologies described herein. Embodiments of the invention arealso directed to systems and methods of acquiring and storing mappingdata in a database, and to systems and methods of providing access toand use of stored mapping data by subscribing users. Embodiments of theinvention are directed to generating a map of the detected utilities,and incorporating mapping data within a GIS or other geographicreference system. A 2-D map and/or a 3-D map of detected utilities canbe generated. Utility maps and other data associated with physicalparameters of the subsurface or of the detected utilities may bedisplayed, such as by use of an operator interface. Additional detailsfor implementing utility mapping and managing utility mapping data inthe context of various embodiments of the invention are disclosed incommonly owned U.S. Pat. No. 6,751,553, which is incorporated herein byreference.

The discussion and illustrations provided herein are presented in anexemplary format, wherein selected embodiments are described andillustrated to present the various aspects of the present invention.Systems, devices, or methods according to the present invention mayinclude one or more of the features, structures, methods, orcombinations thereof described herein. For example, a device or systemmay be implemented to include one or more of the advantageous featuresand/or processes described below. A device or system according to thepresent invention may be implemented to include multiple features and/oraspects illustrated and/or discussed in separate examples and/orillustrations. It is intended that such a device or system need notinclude all of the features described herein, but may be implemented toinclude selected features that provide for useful structures, systems,and/or functionality.

Although only examples of certain functions may be described as beingperformed by circuitry for the sake of brevity, any of the functions,methods, and techniques can be performed using circuitry and methodsdescribed herein, as would be understood by one of ordinary skill in theart.

It is to be understood that even though numerous characteristics ofvarious embodiments have been set forth in the foregoing description,together with details of the structure and function of variousembodiments, this detailed description is illustrative only, and changesmay be made in detail, especially in matters of structure andarrangements of parts illustrated by the various embodiments to the fullextent indicated by the broad general meaning of the terms in which theappended claims are expressed.

What is claimed is:
 1. A method, comprising: generating a series ofscans for a subsurface; forming a series of subsurface images comprisingpixels using the series of scans; creating a derivative image comprisingpixels using the series of subsurface images, wherein each pixel orpixel cluster of the derivative image represents the derivative ofvalues of the pixel or pixel cluster in the series of subsurface images;performing one or more tests on the pixels or pixel clusters of thederivative image; and detecting a subsurface object based on the one ormore tests.
 2. The method according to claim 1, comprising generating anoutput in response to detecting the subsurface obstacle.
 3. The methodaccording to claim 1, comprising generating one or both of a visualoutput and an audible output in response to detecting the subsurfaceobstacle.
 4. The method according to claim 1, wherein creating thederivative image comprises calculating a derivative of a reflected orscattered energy parameter associated with the pixel or pixel cluster ofthe subsurface images.
 5. The method according to claim 1, whereincreating the derivative image comprises: calculating a derivative of aparameter of the subsurface images; and forming the derivative imageusing derivatives of the parameter.
 6. The method according to claim 5,wherein the parameter comprises brightness of the pixel or pixel clusterof the subsurface images.
 7. The method according to claim 1, wherein:creating the derivative image comprises: calculating a derivative of apixel parameter or a pixel cluster parameter of the subsurface images;and forming the derivative image using derivatives of the pixel or pixelcluster parameter; and performing one or more tests on the derivativeimage comprises: determining whether a derivative of each pixel or pixelcluster exceeds a first threshold; and determining whether the previousn derivatives for this pixel or pixel cluster exceeds a secondthreshold, wherein n is an integer.
 8. The method according to claim 7,wherein one or both of the first threshold and the second threshold areadjustable.
 9. The method according to claim 1, wherein: creating thederivative image comprises: calculating a derivative of a pixelparameter or a pixel cluster parameter of the subsurface images; andforming the derivative image using derivatives of the pixel or pixelcluster parameter; and performing one or more tests on the derivativeimage comprises performing at least two of: determining whether thederivative of each pixel or pixel cluster exceeds a first threshold;determining whether the previous n derivatives for this pixel or pixelcluster exceeds a second threshold, wherein n is an integer; determiningif a range associated with each pixel or pixel cluster is less than apredetermined detection range; and determining if a location associatedwith each pixel or pixel cluster is beyond a predetermined position. 10.The method according to claim 1, comprising: forming a mask imagecomprising one or more markers indicative of object detection location;applying the mask image to the subsurface images; and displaying themask image applied to the subsurface images.
 11. The method according toclaim 1, comprising cropping the subsurface images to excise portions ofthe subsurface images beyond a predetermined detection range.
 12. Themethod according to claim 1, wherein: generating the subsurface imagescomprises creating a synthetic aperture radar (SAR) image using theseries of scans; and creating the derivative image comprises creating aderivative image of the SAR image.
 13. A system, comprising: a sensorconfigured to generate a series of scans for a subsurface; and aprocessor coupled to the sensor and configured to execute stored programinstructions that cause the processor to generate a series of images ofthe subsurface using the series of scans, calculate a derivative of aparameter associated with each pixel or pixel cluster of the series ofsubsurface images, create a derivative image using the derivatives ofthe parameter associated with each pixel or pixel cluster of the seriesof subsurface images, perform one or more tests on the derivative image,and detect a subsurface object based on the one or more tests.
 14. Thesystem according to claim 13, wherein the sensor comprises a groundpenetrating radar.
 15. The system according to claim 13, wherein thesensor comprises an acoustic or a seismic sensor.
 16. The systemaccording to claim 13, wherein the sensor comprises an electromagneticsensor.
 17. The system according to claim 13, wherein the sensorcomprises at least one of: a magnetic field generator and a detectorconfigured to detect a return signal; a magnetic resonance (MRI) sourceand a detector configured to detect a return signal; a positron emission(PET) source and a detector configured to detect a return signal; anuclear magnetic resonance (NMR) sensor and a detector configured todetect a return signal; and a time-domain electromagnetic (TDEM) sensorand a detector configured to detect a return signal.
 18. The systemaccording to claim 13, wherein the sensor comprises a housing configuredfor above-ground portability.
 19. The system according to claim 13,wherein the sensor comprises a housing configured for subsurfacedeployment.
 20. The system according to claim 13, comprising a userinterface coupled to the processor, the user interface comprising adisplay for displaying subsurface images and indicia indicative of thedetected subsurface object.
 21. The system according to claim 13,comprising a display coupled to the processor, wherein the processor isconfigured to form a mask image comprising one or more markersindicative of object detection location, apply the mask image to thesubsurface images, and display the mask image applied to the subsurfaceimages on the display.
 22. The system according to claim 13, wherein theprocessor is configured to calculate a derivative of a reflected orscattered energy parameter associated with the pixel or pixel cluster ofthe subsurface images and form the derivative image using derivatives ofthe reflected or scattered energy parameter.
 23. The system according toclaim 13, wherein the processor is configured to calculate a derivativeof brightness associated with the pixel or pixel cluster of thesubsurface images and form the derivative image using derivatives ofbrightness.
 24. The system according to claim 13, wherein the processoris configured to determine whether a derivative of each pixel or pixelcluster exceeds a first threshold, and determine whether the previous nderivatives for this pixel or pixel cluster exceeds a second threshold,wherein n is an integer.
 25. The system according to claim 13, whereinthe processor is configured to perform at least two of: determiningwhether the derivative of each pixel or pixel cluster exceeds a firstthreshold; determining whether the previous n derivatives for this pixelor pixel cluster exceeds a second threshold, wherein n is an integer;determining if a range associated with each pixel or pixel cluster isless than a predetermined detection range; and determining if a locationassociated with each pixel or pixel cluster is beyond a predeterminedposition.
 26. The system according to claim 13, wherein the processor isconfigured to crop the subsurface images to excise portions of thesubsurface images beyond a predetermined detection range.
 27. The systemaccording to claim 13, wherein the processor is configured to create asynthetic aperture radar (SAR) image using the series of scans, andcreate a derivative image of the SAR image.